Device and method for internally and externally assessing whitelists

ABSTRACT

A white list inside or outside determining apparatus includes: a first feature data extracting unit which extracts first feature data from an image by using a first transformation formula created based on preliminary learning images; a second feature data extracting unit which extracts second feature data from an image by using a second transformation formula created from the preliminary learning images and application learning images; a first matching unit which performs matching between a registration image and a collation image by using the first transformation formula; and a second matching unit which performs matching between a registration image and a collation image by using the second transformation formula. Weights of a matching result of the first matching unit and a matching result of the second matching unit are changed according to the number of preliminary learning images and the number of application learning images.

TECHNICAL FIELD

The present invention relates to a white list inside or outsidedetermining apparatus and method capable of accurately determiningwhether a person acquired in a new installation environment is a personregistered in a white list even when the installation environment ischanged.

BACKGROUND ART

FIG. 12 shows a related-art white list inside or outside determiningapparatus and method. In a related-art example, a learning unit 121obtains a subspace transformation formula 123 from a lot of face imagesfor learning by preliminary learning 122. Next, using the subspacetransformation formula 123, a feature amount of an image of a person tobe registered in a white list input from a registration face input 125is obtained by registration face feature data extraction 126 and is heldin a registration face feature data group 127.

On the other hand, when a face image to be collated is input from acollation face input 129, a feature amount of a face to be collated isextracted by collation face feature data extraction 130, and matchingwith a feature amount of a face registered in the registration facefeature data group 127 is performed by a matching unit 131.

As a result, an identical determining unit 132 determines whether thefeature amount of the collation face is identical to the feature amountof a face image of the white list held in the registration face featuredata group 127.

Also, Patent Document 1 shows a method for acquiring a feature amount bycreating a subspace in consideration of an aging change or by creatingsubspaces for respective regions of a face, rather than obtaining afeature amount of the entire face.

Further, there is a white list inside or outside determining apparatuscapable of authenticating an individual even when a light sourcechanges, by a method (FisherFace) for obtaining an eigenface (PCA) andthen minimizing within-class variance and maximizing between-classvariance (FLD) and further facilitating calculation of FLD (Non-PatentDocument 1).

Hereinafter, PCA (Principal Component Analysis), FLD (Fisher's FirstDiscriminant) and a kernel fisher method described in Non-PatentDocument 1 will be described briefly.

PCA is means for reducing dimensionality of image space by atransformation formula in which Mathematical Formula 3 is obtained whenN face images are expressed by Mathematical Formula 1 and a variancematrix of images is expressed by Mathematical Formula 2.

$\begin{matrix}{\mspace{79mu}\left\{ \begin{matrix}x_{1} & x_{2} & \ldots & \left. x_{N} \right\}\end{matrix} \right.} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 1} \right\rbrack \\{\mspace{79mu}{S_{T} = {\sum\limits_{K = 1}^{N}{\left( {x_{k} - \mu} \right)\left( {x_{k} - \mu} \right)^{T}}}}} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 2} \right\rbrack \\{W_{PCA} = {{\arg\;{\max\limits_{W}{{W^{T}S_{T}W}}}} = \begin{bmatrix}w_{1} & w_{2} & \ldots & w_{m_{p}}\end{bmatrix}}} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 3} \right\rbrack\end{matrix}$

Also, FLD is means for minimizing within-person variance and maximizingbetween-person variance, and a transformation formula of MathematicalFormula 6 is obtained when the between-person variance is expressed byMathematical Formula 4 and the within-person variance is expressed byMathematical Formula 5.

$\begin{matrix}{S_{B} = {\sum\limits_{i = 1}^{C}{{N_{i}\left( {\mu_{i} - \mu} \right)}\left( {\mu_{i} - \mu} \right)^{T}}}} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 4} \right\rbrack \\{S_{W} = {\sum\limits_{i = 1}^{C}{\sum\limits_{x_{k} \in X_{i}}{\left( {x_{k} - \mu_{i}} \right)\left( {x_{k} - \mu_{i}} \right)^{T}}}}} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 5} \right\rbrack \\{W_{FLD} = {\arg\;{\max\limits_{W}\frac{{W^{T}S_{B}W}}{{W^{T}S_{W}W}}}}} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 6} \right\rbrack\end{matrix}$

Finally, a FisherFace method is a method capable of facilitatingcalculation of FAD even when the number of images is small, and isexpressed by Mathematical Formula 7, Mathematical Formula 8 andMathematical Formula 9.

$\begin{matrix}{W_{FisherFace}^{T} = {{W_{fld}^{T}W_{pca}^{T}} = \begin{bmatrix}w_{1} & w_{2} & \ldots & w_{c}\end{bmatrix}}} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 7} \right\rbrack \\{\mspace{79mu}{W_{fld} = {\arg\;{\max\limits_{W}\frac{{W^{T}W_{pca}^{T}S_{B}W_{pca}W}}{{W^{T}W_{pca}^{T}S_{W}W_{pca}W}}}}}} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 8} \right\rbrack \\{\mspace{79mu}{{W_{pca} = {\arg\;{\max\limits_{W}{{W^{T}S_{T}W}}}}}\mspace{79mu}{W_{fld} = {\arg\;{\max\limits_{W}\frac{{W^{T}W_{pca}^{T}S_{B}W_{pca}W}}{{W^{T}W_{pca}^{T}S_{W}W_{pca}W}}}}}}} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 9} \right\rbrack\end{matrix}$

Accordingly, dimensionality of W_(FLD) can be reduced to c−1.

RELATED ART DOCUMENTS Patent Documents

-   Patent Document 1: JP A-11-175718

Non-Patent Documents

-   Non-Patent Document 1: “Eigenfaces vs. Fisherfaces: Recognition    Using Class Specific First Projection”, Peter N. Belhumer, Joao P.    Hespanha, and David J. Kriegman, IEEE Transaction on Pattern    Analysis and Machine Intelligence, vol. 19, No. 7 July, pp 711-720

SUMMARY OF THE INVENTION Problem to be Solved by the Invention

However, in the related art, as shown in FIG. 13, when photographingconditions of a face image used in the learning unit 121, a face imageregistered in a white list used in a registration unit 124 and a faceimage of a collation unit 128 differ from one another, conditions forobtaining feature amounts also differ, which results in a problem ofincreasing false alarm. Also, when registration data (black list)increases, it becomes difficult to distinguish a person registered inthe white list, which results in a problem of increasing false alarm.

Further, in a case in which a transformation formula for creatingsubspace is created by images photographed in a real environment, if thenumber of images for creating subspace is small, a stable result cannotbe obtained.

An object of the invention is to solve these problems, and is to providea white list inside or outside determining apparatus and method capableof determining whether a collation person is registered in a white listeven when photographing conditions in a learning unit, a registrationunit and a collation unit differ or the number of images adapted for adifferent environment is small.

Means for Solving the Problem

The present invention provides a white list inside or outsidedetermining apparatus including: first feature data extracting means forextracting first feature data from an image by using a firsttransformation formula created based on a plurality of preliminarylearning images; second feature data extracting means for extractingsecond feature data from an image by using a second transformationformula created from the preliminary learning images and a plurality ofapplication learning images; first matching means for performingmatching between a registration image and a collation image by using thefirst transformation formula; and second matching means for performingmatching between a registration image and a collation image by using thesecond transformation formula, wherein weights of a matching result ofthe first matching means and a matching result of the second matchingmeans are changed according to the number of preliminary learning imagesand the number of application learning images.

With this configuration, weighting of the matching result of the firstmatching means and the matching result of the second matching means canbe changed according to a ratio between the number of preliminarylearning images and the number of application learning images.

In the white list inside or outside determining apparatus of theinvention, the weight of the matching result of the second matchingmeans is decreased as the number of application learning images becomessmaller.

With this configuration, even when the number of application learningimages is small and hence the matching result by the secondtransformation formula does not become stable, a stable result can beobtained by increasing the weight of matching by the firsttransformation formula. Further, when the number of application learningimages is large, a matching result adapted for an environment in whichthe application learning images are acquired can be obtained byincreasing the weight of the matching result by the secondtransformation formula.

Further, in the white list inside or outside determining apparatus ofthe invention, the collation image is registered as the registrationimage when a degree of similarity is larger than a predeterminedthreshold value.

With this configuration, the collation image can be registered as a newregistration image when the degree of similarity between the collationimage and the registration image is larger than the predeterminedthreshold value.

Advantages of the Invention

In the invention, in the case of determining whether the collation imageacquired in a real environment is a person registered in a white list,large weight is placed on the transformation formula created inpreliminary learning when the number of application learning imagesacquired in the real environment is small, so that a stabledetermination result can be obtained, and large weight is placed on thetransformation formula created in application learning images when thenumber of application learning images acquired in the real environmentis large, so that a determination result adapted for the realenvironment can be obtained.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block configuration diagram showing a configuration of afirst embodiment of the invention.

FIG. 2 is an explanatory diagram showing a flow of processing of thefirst embodiment of the invention.

FIG. 3 is a flow diagram showing a flow of initialization processing ofthe first embodiment of the invention.

FIG. 4 is a flow diagram showing a flow of the first embodiment of theinvention.

FIG. 5 is a flow diagram showing a flow of the first determination of acollation face of the first embodiment of the invention.

FIG. 6 is a flow diagram showing a flow of the second determination of acollation face of the first embodiment of the invention.

FIG. 7 is an explanatory diagram showing a feature of the firstembodiment of the invention.

FIG. 8 is a block configuration diagram showing a configuration of asecond embodiment of the invention.

FIG. 9 is a flow diagram showing a flow of initialization processing ofthe second embodiment of the invention.

FIG. 10 is an explanatory diagram showing a usage situation of thesecond embodiment of the invention.

FIG. 11 is an explanatory diagram showing a usage situation of thesecond embodiment of the invention.

FIG. 12 is a block configuration diagram of a conventional white listinside or outside determining apparatus.

FIG. 13 is an explanatory diagram of the conventional white list insideor outside determining apparatus.

MODE FOR CARRYING OUT THE INVENTION First Embodiment

A first embodiment according to the invention will hereinafter bedescribed in detail by the drawings. FIG. 1 is a block diagram showingthe first embodiment of a white list inside or outside determiningapparatus of the invention.

In FIG. 1, initialization processing 1 includes preliminary learningmeans 15, a first transformation formula 16 created by the preliminarylearning means 15, registration face sample application learning means17, and a second transformation formula 18 created by the registrationface sample application learning means 17.

A configuration other than the initialization processing 1 of the whitelist inside or outside determining apparatus includes registration faceinput means 2 for inputting a face image of a white list, first featuredata extracting means 3 for transforming the input face image of thewhite list by the first transformation formula 16, a first feature datagroup 5 for holding the extracted first feature data, second featuredata extracting means 4 for transforming the input face image of thewhite list by the second transformation formula 18, a second featuredata group 6 for holding the extracted second feature data, collationface input means 7 for inputting a collation face, first and secondfeature data extracting means 8 for extracting first feature data andsecond feature data of a collation face image, first matching means 9for performing matching between the first feature data group and thefirst feature data of the collation face extracted, second matchingmeans 10 for performing matching between the second feature data groupand the second feature data of the collation face extracted, andidentical determining means 11 for determining whether or not thecollation image is registered in the white list using results of thefirst matching means 9 and the second matching means 10.

Next, an outline of the invention will be described using FIG. 2. Amethod for making a white list inside or outside determination byminimizing within-person variance and also maximizing between-personvariance using a FisherFace method described in Non-Patent Document 1will be described first.

The FisherFace method is a method capable of stably doing calculationfor minimizing within-class variance and maximizing between-classvariance after obtaining a learning result by PCA even when the numberof images given every class is small.

In the case of using this method, a transformation formula forminimizing within-person variance and maximizing between-person variancewith respect to a face image of a person registered in the white listcan be obtained.

First, feature space is created by a PCA (Principal Component Analysis)method using numerous face images (for example, for 3000 persons). Also,by FLD, within-person variance is minimized and between-person varianceis maximized (numeral 21 of FIG. 2). As a result, Mathematical Formula10 is obtained as a first transformation formula (numeral 16 of FIG. 2).W _(fld)  [Mathematical Formula 10]

Next, with respect to feature data after first transformation of a faceimage acquired in an environment in which a white list collatingapparatus is installed, within-person variance of feature space isminimized and between-person variance is maximized by the FisherFacemethod (numeral 23 of FIG. 2). As a result, Mathematical Formula 11 isobtained as a second transformation formula (numeral 18 of FIG. 2).W _(FisherFace)  [Mathematical Formula 11]

In the case of performing white list face collation, MathematicalFormula 13 is obtained by (Mathematical Formula 10) (numeral 26 of FIG.2) when a face image (numeral 25 of FIG. 2) registered in a white listis expressed by Mathematical Formula 12.F _(w)  [Mathematical Formula 12]V _(w1) =W _(fld) F _(w)  [Mathematical Formula 13]

On the other hand, Mathematical Formula 15 is obtained by (MathematicalFormula 10) (numeral 27 of FIG. 2) when a collation face image isexpressed by Mathematical Formula 14.F _(e)  [Mathematical Formula 14]V _(e1) =W _(fld) F _(e)  [Mathematical Formula 15]

Then, when a threshold value is expressed by W_(ELD), the degree ofsimilarity by the first transformation formula is expressed as follows(numeral 28 of FIG. 2).

$\begin{matrix}{S_{FLD} = {1 - \frac{{V_{e\; 1} - V_{w\; 1}}}{T_{FLD}}}} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 16} \right\rbrack\end{matrix}$

When the degree of similarity becomes minus, it is decided that the faceimages are not similar, and when the degree of similarity becomes plus,this is targeted for consideration.

Similarly, when a feature amount of the white list obtained by thesecond transformation formula is expressed by V_(w2) and a featureamount of a collation face image obtained by the second transformationformula is expressed by V_(e2), the degree of similarity by FisherFaceis expressed as follows (numeral 29 of FIG. 2).

$\begin{matrix}{S_{FisherFace} = {1 - \frac{{V_{e\; 2} - V_{w\; 2}}}{T_{FisherFace}}}} & \left\lbrack {{Mathematical}\mspace{14mu}{Formula}\mspace{14mu} 17} \right\rbrack\end{matrix}$

However, T_(FisherFace) is a threshold value.

When weights are expressed by w1 and w2, the final degree of similaritycan be obtained as follows (numeral 30 of FIG. 2).S=w ₁ −S _(FLD) +w ₂ ×S _(FisherFace)  [Mathematical Formula 18]where,w ₁ +w ₂=1  [Mathematical Formula 19]

Then, when a value of S (Mathematical Formula 18) is more than or equalto a predetermined threshold value, it is decided that a collation faceis registered in the white list, and when the value of S is less thanthe predetermined threshold value, it is decided that a collation faceimage is not within the white list.

At this time, values of w1 and w2 are changed by a ratio between thenumber of face images used in a learning model and the number of imagesacquired in real space.

That is, in the first embodiment of the invention, the final degree ofsimilarity is obtained by changing a contribution ratio of the degree ofsimilarity obtained using the first transformation formula and thedegree of similarity obtained using the second transformation formulaaccording to a ratio between the number of learning images used in thepreliminary learning means 15 and the number of images used in theregistration face sample application learning means 17.

Accordingly, when the number of images at the time of doing registrationface sample application learning is small and a calculated result of thedegree of similarity using the second transformation formula becomesunstable, weight of a result of the degree of similarity using the firsttransformation formula can be increased, so that a stable result can beobtained. On the other hand, when the number of images at the time ofdoing the registration face sample application learning is large, weightof a calculated result of the degree of similarity using the secondtransformation formula can be increased, so that a result adapted for areal environment can be obtained when images for registration facesample application learning are acquired in the real environment.

Next, a flow of processing of the first embodiment of the invention willbe described using FIGS. 1 to 6. First, the initialization processing 1of FIG. 1 will be described. The preliminary learning means 15 createsthe first transformation formula 16 from many learning data using PCAand FLD as described by numeral 21 of FIG. 2. Also, the registrationface sample application learning means creates the second transformationformula 18 from the first transformation formula 16 by the FisherFacemethod using sample images acquired in a real environment.

FIG. 3 shows a flow of the initialization processing 1. In a preliminarylearning data input (S31), numerous face images for, for example, 3000persons for use in preliminary learning are input. Next, in creation ofthe first transformation formula by PCA and FLD (S32), the firsttransformation formula 16 (Mathematical Formula 10) is obtained.

Then, the white list inside or outside determining apparatus is moved ina place operating actually. Then, data acquisition in the realenvironment (S33) is performed. Then, the second transformation formula18 (Mathematical Formula 11) is created (S34) by the FisherFace method.

Next, registration of a face image registered in a white list will bedescribed. In FIG. 1, an image registered in the white list is inputfrom the registration face input means 2. Then, first feature data isextracted using the first transformation formula 16 by the first featuredata extracting means 3 and is held in the first feature data group 5.

Also, in the image registered in the white list and input from theregistration face input means 2, second feature data is extracted usingthe second transformation formula 18 by the second feature dataextracting means 4 and is held in the second feature data group 6.

Next, when the white list inside or outside determining apparatus isinstalled in the real environment, a face image for collation of aperson in the real environment is input by the collation face inputmeans 7.

In the face image for collation, first feature data and second featuredata are created using the first transformation formula 16 and thesecond transformation formula 18 by the first and second feature dataextracting means 8.

Then, matching between the first feature data of the face image forcollation and feature data held in the first feature data group 5 isperformed by the first matching means 9.

Similarly, matching between the second feature data of the face imagefor collation and feature data held in the second feature data group 6is performed by the second matching means 10.

Then, the identical determining means 11 determines whether or not theface image for collation matches with the face image registered in thewhite list.

FIG. 4 is a flowchart showing a range from registration of the faceimage of a person registered in the white list to determination of theface image for collation.

In a registration face input (S41), a face image registered as the whitelist is input. In first data extraction (S42), a feature amount of aface image to be registered in the white list is extracted using thefirst transformation formula 16. Then, in first data holding (S43), thefeature amount is registered in the first feature data group 5.

In second data extraction (S44), a feature amount of a face image to beregistered in the white list is extracted using the secondtransformation formula 18. Then, in second data holding (S45), thefeature amount is registered in the second feature data group 6.

Then, in determination (S46) of a collation face, the collation face isdetermined.

FIGS. 5 and 6 show the inside of determination (S46) of the collationface, and FIG. 5 shows the first technique and FIG. 6 shows the secondtechnique.

In FIG. 5, in a collation face input (S51), collation face data fordetermining whether or not to be a person registered in the white listis input by the collation face input means 7 in the real environment.

In first and second feature data extraction (S52), first and secondfeature data of the collation face are extracted using the firsttransformation formula 16 and the second transformation formula 18 bythe first and second feature data extracting means 8.

In first feature data reading (S53), a first feature of a face imageregistered in the white list and held in the first feature data group 5is read.

In matching with collation data (S54), matching between a feature amountof the collation face and feature data held in the first feature datagroup 5 is performed by the first matching means 9.

At this time, in extraction of (M) data whose degree of similarity is aor more (S55), a person registered in the white list, whose degree ofsimilarity is a or more, is selected by a result of matching in S54.

M second feature amounts are read out of the second feature data group 6(S56).

In second matching with collation data (S57), matching between a featureamount of the collation face and M second feature data read out by S56is performed by the second matching means 10.

Then, a person with the highest degree of similarity and registered inthe white list is selected by extraction of data with the highest degreeof similarity (S58).

In identical determination (S59), it is determined that a collationimage is a person registered in the white list when the degree ofsimilarity is more than or equal to a predetermined threshold value (β).

As a method of determination in this case, the method described in(Mathematical Formula 16) to (Mathematical Formula 19) is used, andvalues of w1 and w2 are determined by a ratio between the number of faceimages used in the preliminary learning means 15 and the number ofimages acquired in the real environment used in the registration facesample application learning means 17 acquired in the real environment.

For example, the values of w1 and w2 are set so that (w1:w2)=(0.2:0.8)is satisfied when the number of persons of registration data is largeand (w1:w2)=(0.5:0.5) is satisfied when the number of persons ofregistration data is small.

Consequently, when the number of registration face samples acquired inthe real environment is small, the degree of similarity obtained usingthe first transformation formula has priority over the degree ofsimilarity obtained using the second transformation formula, so that astable result can be obtained.

Next, the second technique of determination (S46) of the collation faceof FIG. 4 will be described using FIG. 6.

In FIG. 6, the portion ranging to first feature data reading (S63) issimilar to that of the first technique of FIG. 5.

Also, in second feature data reading (S64), all the second featureamounts held in the second feature data group 6 are read by the secondmatching means 10.

In first matching with collation data (S65), matching between a featureamount of a collation image extracted by the first and second featuredata extracting means 8 and a feature amount read out of the firstfeature data group 5 is performed by the first matching means 9.

In second matching with collation data (S66), matching between a featureamount of a collation image extracted by the first and second featuredata extracting means 8 and a feature amount read out of the secondfeature data group 6 is performed by the second matching means 10.

Finally, the identical determining means (S67) determines whether or notthe collation image is identical to the face image registered in thewhite list using these degrees of similarity.

A method of identical determination is the same as the method describedin FIG. 5.

FIG. 7 shows advantages of the invention. For example, even when thereare many frontal faces as preliminary registration data, a change inface direction can be handled by the second transformation formula.Also, when the number of images other than the frontal faces acquired inthe real environment is small, a situation in which a system becomesunstable due to too dependence on the second transformation formula canbe reduced.

Also, when an illumination condition differs from that of preliminarylearning, there is an advantage capable of covering a difference in anactual illumination condition by the second transformation formula.

Second Embodiment

FIG. 8 shows a second embodiment of the invention. The second embodimentnewly has updating means 81 of a registration face as compared with thefirst embodiment.

The updating means 81 of the registration face can also set a face imageacquired in a real environment at a face image to be registered in awhite list when the degree of similarity is a predetermined thresholdvalue (γ, γ≧β) in the first embodiment. Accordingly, the face imageregistered in the white list can be adapted for the real environment.

FIG. 9 shows a flow of the second embodiment of the invention. When thedegree of similarity exceeds a predetermined threshold value (γ, γ≧β) asa result of identical determination in the second embodiment, an imageacquired in the real environment is registered in the white list (S91).The other steps are similar to those of FIG. 5.

FIGS. 10 and 11 show a usage situation of the invention. FIG. 10 showsan unauthorized outing of a patient in a hospital by a camera 1) orintrusion of an unregistered person into the hospital by a camera 2).

Since an inpatient has many occasions photographed by the camera, oncethe inpatient is registered in a white list, the inpatient is recognizedand updated as a person registered in the white list many times, so thatit becomes easy to detect the inpatient in the case of attempting to goout of the front door without leave.

On the other hand, persons other than the inpatients or normal workersin the hospital are not collated as the person registered in the whitelist, so that intrusion of a suspicious person can be detected easily.

FIG. 11 shows an example of checking the whereabouts of inpatients orworkers in a hospital. When a name of a patient or a hospital worker isinput from a personal computer of an office room, an applicable personis sought and displayed from a camera 3) of a rehabilitation room, acamera 4) set on a corridor, a camera 5) of a hospital room, a camera 6)installed in a waiting room, cameras 7) and 8) installed at the frontdoor, etc. for a period of the last several hours (or several minutes).

In addition, in FIG. 3, the white list inside or outside determiningapparatus is moved in a place operating actually, but is not necessarilymoved.

The invention has been described in detail with reference to thespecific embodiments, but it is apparent to those skilled in the artthat various changes or modifications can be made without departing fromthe spirit and scope of the invention.

The present application is based on Japanese patent application (patentapplication No. 2010-214883) filed on Sep. 27, 2010, and the contents ofthe patent application are hereby incorporated by reference.

INDUSTRIAL APPLICABILITY

The invention is the invention according to a white list inside oroutside determining apparatus and method capable of detecting, forexample, intrusion of a suspicious person or the whereabouts in ahospital of a person registered as a white list in the hospital etc.

DESCRIPTION OF REFERENCE SIGNS

-   -   1 INITIALIZATION PROCESSING    -   2 REGISTRATION FACE INPUT MEANS    -   3 FIRST FEATURE DATA EXTRACTING MEANS    -   4 SECOND FEATURE DATA EXTRACTING MEANS    -   5 FIRST FEATURE DATA GROUP    -   6 SECOND FEATURE DATA GROUP    -   7 COLLATION FACE INPUT MEANS    -   8 FIRST AND SECOND FEATURE DATA EXTRACTING MEANS    -   9 FIRST MATCHING MEANS    -   10 SECOND MATCHING MEANS    -   11 IDENTICAL DETERMINING MEANS

The invention claimed is:
 1. A white list inside or outside determiningapparatus comprising: a first feature data extracting unit whichextracts first feature data from an image by using a firsttransformation formula created based on a plurality of preliminarylearning images; a second feature data extracting unit which extractssecond feature data from an image by using a second transformationformula created from the preliminary learning images and a plurality ofapplication learning images; a first matching unit which performsmatching between a registration image and a collation image by using thefirst transformation formula; and a second matching unit which performsmatching between a registration image and a collation image by using thesecond transformation formula, wherein weights of a matching result ofthe first matching unit and a matching result of the second matchingunit are changed according to the number of preliminary learning imagesand the number of application learning images.
 2. The white list insideor outside determining apparatus according to claim 1, wherein theweight of the matching result of the second matching unit is decreasedas the number of application learning images becomes smaller.
 3. Thewhite list inside or outside determining apparatus according to claim 1,wherein the collation image is registered as the registration image whena degree of similarity is larger than a predetermined threshold value.4. A white list inside or outside determining method comprising:extracting first feature data from an image by using a firsttransformation formula created based on a plurality of preliminarylearning images; extracting second feature data from an image by using asecond transformation formula created from the preliminary learningimages and a plurality of application learning images; performing afirst matching between a registration image and a collation image byusing the first transformation formula; and performing a second matchingbetween a registration image and a collation image by using the secondtransformation formula, wherein weights of a matching result of thefirst matching and a matching result of the second matching are changedaccording to the number of preliminary learning images and the number ofapplication learning images.